Arabic (Indian) Handwritten‏ ‏Digits Recognition Using Multi feature and KNN Classifier

تقدم هذه الورقة نظام التعرف على أرقام مكتوبة بخط اليد العربية على أساس الجمع بين أساليب الاستخراج متعددة المزايا، مثل   الملف الجانبي العلوي، ورأسية _ الإسقاط الأفقي وتحويل جيب التمام منفصلة مع  الانحراف المعياري.   يتم استخراج هذه الميزات من الصورة بعد تقسيمها الى عدة كتل.   المصنف KNN يستخدم لغرض التصنيف. يتم اختبار هذا العمل مع قاعدة بيانات ADBase القياسية (الأرقام العربية)، والتي تتكون من  70,000 أرقام  تم كتابتها من قبل 700 شخص مختلف.  في النظام المقترح يستخدم 60000  صورة رقم  لمرحلة التدريب و 10000 صورة رقم في مرحلة الاختبار. حقق هذا العمل دقة تعرف على  الارقام مقدارها  97.32٪.This paper presents an Arabic (Indian)  handwritten digit recognition system based on combining  multi feature  extraction methods, such a upper_lower  profile, Vertical _ Horizontal projection and Discrete Cosine Transform (DCT) with Standard Deviation σi called (DCT_SD)  methods. These  features are extracted from the image  after dividing it by several blocks. KNN classifier used  for classification purpose. This work is tested with the ADBase standard database (Arabic numerals),  which consist of 70,000 digits were 700 different writers write  it. In proposing system used 60000 digits, images for training phase and 10000 digits, images in testing phase. This work  achieved  97.32%  recognition  Accurac

Arabic Handwritten Alphanumeric Character Recognition using Fuzzy Attributed Turning Functions

In this paper, we present a novel method for recognition of unconstrained handwritten Arabic alphanumeric characters. The algorithm binarizes the character image, smoothes it and extracts its contour. A novel approach for polygonal approximation of handwritten character contours is applied. The directions and length features are extracted from the polygonal approximation. These features are used to build character models in the training phase. For the recognition purpose, we introduce Fuzzy Attributed Turning Functions (FATF) and define a dissimilarity measure based on FATF for comparing polygonal shapes. Experimental results demonstrate the effectiveness of our algorithm for recognition of handwritten Arabic characters. We have obtained around 98% accuracy for Arabic handwritten characters and more than 97% accuracy for handwritten Arabic numerals

Handwritten Arabic Digit Recognition Using Convolutional Neural Network

In Computer vision systems, computer vision works by imitating humans in their vision way which is known as the human vision system (HVS). In HVS, humans use their eyes and brains in order to see and classify any object around them. Hence, computer vision systems imitate HSV by developing several algorithms for classifying images and objects. The main goal of this paper is to propose a model for identifying and classifying the Arabic handwritten digits with high accuracy.  The concept of deep learning via the convolutional neural network (CNN) with the ADBase database is used to achieve the goal. The training is done by having a 3*3 and 5*5 filters. Basically, while the classification phase distinct learning rates are used to train the network. The obtained results are encouraging and promising

Comparison of Handwritten Recognition Methods on Arabic and Latin Characters

In this article, both machine learning techniques and deep learning methods were applied on the digit datasets created using the Arabic and Latin alphabets, and the performances of the methods were compared. Each method was tested with various parameters and the results were analyzed. In addition, with this study, the recognizability of handwritten numeral datasets created using different alphabets was also observed. For experiments, an Arabic alphabet handwritten digit dataset (60,000 training and 10,000 testings) and a Latin alphabet handwritten digit dataset (60,000 training and 10,000 testings) were used. When the results of the experiment are examined, it is seen that successful results are obtained in the classification made with the MADBase dataset in some methods and in the classification made with the MNIST dataset in some methods. As a result, it can be stated that the handwriting character recognition success of a method cannot be measured only by the classification made on a dataset. Also, the digits written in the Arabic alphabet appear to be almost more recognizable than the digits written in the Latin alphabet

Cyclic Self-Organizing Map for Object Recognition

Object recognition is an important machine learning (ML) application. To have a robust ML application, we need three major steps: (1) preprocessing (i.e. preparing the data for the ML algorithms); (2) using appropriate segmentation and feature extraction algorithms to abstract the core features data and (3) applying feature classification or feature recognition algorithms. The quality of the ML algorithm depends on a good representation of the data. Data representation requires the extraction of features with an appropriate learning rate. Learning rate influences how the algorithm will learn about the data or how the data will be processed and treated. Generally, this parameter is found on a trial-and-error basis and scholars sometimes set it to be constant. This paper presents a new optimization technique for object recognition problems called Cyclic-SOM by accelerating the learning process of the self-organizing map (SOM) using a non-constant learning rate. SOM uses the Euclidean distance to measure the similarity between the inputs and the features maps. Our algorithm considers image correlation using mean absolute difference instead of traditional Euclidean distance. It uses cyclical learning rates to get high performance with a better recognition rate. Cyclic-SOM possesses the following merits: (1) it accelerates the learning process and eliminates the need to experimentally find the best values and schedule for the learning rates; (2) it offers one form of improvement in both results and training; (3) it requires no manual tuning of the learning rate and appears robust to noisy gradient information, different model architecture choices, various data modalities and selection of hyper-parameters and (4) it shows promising results compared to other methods on different datasets. Three wide benchmark databases illustrate the efficiency of the proposed technique: AHD Base for Arabic digits, MNIST for English digits, and CMU-PIE for faces

Full depth CNN classifier for handwritten and license plate characters recognition

Character recognition is an important research field of interest for many applications. In recent years, deep learning has made breakthroughs in image classification, especially for character recognition. However, convolutional neural networks (CNN) still deliver state-of-the-art results in this area. Motivated by the success of CNNs, this paper proposes a simple novel full depth stacked CNN architecture for Latin and Arabic handwritten alphanumeric characters that is also utilized for license plate (LP) characters recognition. The proposed architecture is constructed by four convolutional layers, two max-pooling layers, and one fully connected layer. This architecture is low-complex, fast, reliable and achieves very promising classification accuracy that may move the field forward in terms of low complexity, high accuracy and full feature extraction. The proposed approach is tested on four benchmarks for handwritten character datasets, Fashion-MNIST dataset, public LP character datasets and a newly introduced real LP isolated character dataset. The proposed approach tests report an error of only 0.28% for MNIST, 0.34% for MAHDB, 1.45% for AHCD, 3.81% for AIA9K, 5.00% for Fashion-MNIST, 0.26% for Saudi license plate character and 0.97% for Latin license plate characters datasets. The license plate characters include license plates from Turkey (TR), Europe (EU), USA, United Arab Emirates (UAE) and Kingdom of Saudi Arabia (KSA)

Advancements and Challenges in Arabic Optical Character Recognition: A Comprehensive Survey

Optical character recognition (OCR) is a vital process that involves the extraction of handwritten or printed text from scanned or printed images, converting it into a format that can be understood and processed by machines. This enables further data processing activities such as searching and editing. The automatic extraction of text through OCR plays a crucial role in digitizing documents, enhancing productivity, improving accessibility, and preserving historical records. This paper seeks to offer an exhaustive review of contemporary applications, methodologies, and challenges associated with Arabic Optical Character Recognition (OCR). A thorough analysis is conducted on prevailing techniques utilized throughout the OCR process, with a dedicated effort to discern the most efficacious approaches that demonstrate enhanced outcomes. To ensure a thorough evaluation, a meticulous keyword-search methodology is adopted, encompassing a comprehensive analysis of articles relevant to Arabic OCR, including both backward and forward citation reviews. In addition to presenting cutting-edge techniques and methods, this paper critically identifies research gaps within the realm of Arabic OCR. By highlighting these gaps, we shed light on potential areas for future exploration and development, thereby guiding researchers toward promising avenues in the field of Arabic OCR. The outcomes of this study provide valuable insights for researchers, practitioners, and stakeholders involved in Arabic OCR, ultimately fostering advancements in the field and facilitating the creation of more accurate and efficient OCR systems for the Arabic language

A New Approach to Synthetic Image Evaluation

This study is dedicated to enhancing the effectiveness of Optical Character Recognition (OCR) systems, with a special emphasis on Arabic handwritten digit recognition. The choice to focus on Arabic handwritten digits is twofold: first, there has been relatively less research conducted in this area compared to its English counterparts; second, the recognition of Arabic handwritten digits presents more challenges due to the inherent similarities between different Arabic digits.OCR systems, engineered to decipher both printed and handwritten text, often face difficulties in accurately identifying low-quality or distorted handwritten text. The quality of the input image and the complexity of the text significantly influence their performance. However, data augmentation strategies can notably improve these systems\u27 performance. These strategies generate new images that closely resemble the original ones, albeit with minor variations, thereby enriching the model\u27s learning and enhancing its adaptability. The research found Conditional Variational Autoencoders (C-VAE) and Conditional Generative Adversarial Networks (C-GAN) to be particularly effective in this context. These two generative models stand out due to their superior image generation and feature extraction capabilities. A significant contribution of the study has been the formulation of the Synthetic Image Evaluation Procedure, a systematic approach designed to evaluate and amplify the generative models\u27 image generation abilities. This procedure facilitates the extraction of meaningful features, computation of the Fréchet Inception Distance (LFID) score, and supports hyper-parameter optimization and model modifications